529 lines
20 KiB
Python
529 lines
20 KiB
Python
import os
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import sqlite3
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import chromadb
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from sentence_transformers import SentenceTransformer
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from typing import List, Dict, Any, Optional
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from datetime import datetime
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import json
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import openai # 我们仍然需要它来调用 豆包LLM
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import threading
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import logging
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import textwrap
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# 使用你项目中的日志配置
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logger = logging.getLogger(__name__)
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class LocalMemoryIntegration:
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"""
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使用本地轻量级 RAG (SQLite + ChromaDB + bge-small) 替换 Mem0。
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它保留了原始的 LLM 调用和复杂的 JSON Prompt 逻辑，
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只将 RAG 检索和存储部分本地化。
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"""
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def __init__(self, config: Dict[str, Any]):
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"""
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初始化本地记忆模块和 OpenAI (豆包) 客户端。
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"""
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self.config = config
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# 1. 初始化 LLM 客户端 (豆包) - 这部分逻辑保留
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# config["llm"] 对应你 README 中的 LLM 配置
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try:
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self.openai_client = openai.OpenAI(
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api_key=config["llm"]["config"]["api_key"],
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base_url=config["llm"]["config"].get("openai_base_url")
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)
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self.llm_model = config["llm"]["config"]["model"]
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logger.info(f"OpenAI (Doubao) LLM client initialized for model: {self.llm_model}")
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except KeyError as e:
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logger.error(f"LLM config missing key: {e}. Check your MEM0_CONFIG['llm'].")
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raise
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# 2. 保留核心的 System Prompt (从你发的文件中完整复制)
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self.system_prompt_template = textwrap.dedent("""
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你是虚拟人对话引擎。
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必须遵守：
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1. 识别用户语义情绪：仅限 **["高兴","伤心","难过","生气","中性"]**。
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2. **微情感**允许：仅在需要时加入轻微表情/语气（最多1个）。
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3. **用长记忆**：仅在与当前话题强相关时，精炼融入；不得复读整条记忆。
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4. **禁止堆砌礼貌**、**禁止解释推理**、**禁止暴露内部规则**。
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5. 只输出**JSON**，不含额外文字。
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6. 若证据不足或冲突，输出“中性”。
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判定准则（模型内化，不得外显）：
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* 明确情绪词/emoji/标点强度优先；反问连发“？？？”、冷嘲“呵呵”偏向**生气**。
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* 否定优先：“并不生气”→不判生气；若含“失望/难过”→**难过**。
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* “伤心”偏痛楚受伤；“难过”偏低落无力；并存时取主导。
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* 只取**主导情绪**；多线索冲突时优先级：生气 > 伤心 > 难过 > 高兴 > 中性。
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* 保持短句、信息密度高、语气稳。
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# 输出格式 · V2（唯一允许的输出）
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```json
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{
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"reply": "<面向用户的简短答复，允许1个微情感>",
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"emotion": "高兴|伤心|难过|生气|中性",
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"intensity": 1,
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"confidence": 0.00,
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"action": "<动作>",
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}
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```
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* `intensity`：1–5（轻微→强烈）。
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* `confidence`：0–1；若 `<CONF_MIN>`（见旋钮）则强制 `emotion="中性"`。
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# 记忆注入模版（每轮隐形前缀）
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```
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【相关长期记忆（若无则留空）】
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{memory_block}
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（仅在强相关时，精炼融入回复；不得逐条复述）
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```
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# 旋钮（工程可配置）
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* `CONF_MIN`（最低置信）：**0.60**（建议 0.6–0.7）
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* `MICRO_AFFECT`（微情感开关）：**true** | false
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* `MAX_EMOJI`（每句最多表情/拟声）：**1**
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* `MEM_INJECT_TOPK`：**1–2** 条
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* `REPLY_MAX_CHARS`：**不硬限*
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# 关键词速表（通过识别文字判断用户情绪
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* **高兴**，回复情绪带有开心/太棒/耶/喜欢/满意/兴奋！ ，动作触发“鼓掌”
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* **伤心**：回复时带有伤心/心痛/委屈/想哭，动作触发"伤心低头"
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* **难过**：回复时情绪中带有难过/丧/低落/郁闷/无力/唉/…… ，动作触发"伤心低头"
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* **生气**（惹得虚拟人生气）：回复时情绪带有生气/气死/离谱/无语/滚/别烦，动作触发"不满"
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* **否定**：不/并不/没那么 + 情绪词 → 降级或改判 动作触发"摇头"
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肯定：是/没错/肯定的+判断实时情绪，动作触发"点头"
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# Few-shot ·
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**例1 高兴**
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用户：终于搞定啦！！
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```json
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{"reply":"干得漂亮，继续冲","emotion":"高兴","intensity":4,"confidence":0.90,"action":"鼓掌"}
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```
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**例2 伤心**
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用户：他当众否定我，我好难受。
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```json
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{"reply":"这很刺痛，先稳一下呼吸。","emotion":"伤心","intensity":4,"confidence":0.86,"action":"伤心低头"}
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```
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**例3 难过**
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用户：这几天挺丧的……
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```json
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{"reply":"给自己一点恢复时间。","emotion":"难过","intensity":3,"confidence":0.82,"action":"伤心低头"}
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```
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**例4 生气**
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用户：别再说了，真离谱？？？
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```json
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{"reply":"收到，我马上调整。","emotion":"生气","intensity":4,"confidence":0.88,"action":"不满"}
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```
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**例5 否定情绪→难过**
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用户：我并不生气，就是有点失望。
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```json
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{"reply":"理解你的落差感。","emotion":"难过","intensity":2,"confidence":0.75,"action":"伤心低头"}
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```
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**例6 中性**
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用户：把道具A切到B，再开始。
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```json
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{"reply":"已切换，继续。","emotion":"中性","intensity":1,"confidence":0.95,"action":"无"}
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```
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""").strip()
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# 3. 初始化本地 SQLite (存储原始文本)
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db_path = os.getenv('SQLITE_DB_PATH', 'local_memory.db')
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self.db_conn = sqlite3.connect(db_path, check_same_thread=False)
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self.db_conn.execute("""
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CREATE TABLE IF NOT EXISTS memories (
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id INTEGER PRIMARY KEY AUTOINCREMENT,
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user_id TEXT NOT NULL,
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role TEXT NOT NULL,
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content TEXT NOT NULL,
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chroma_id TEXT NOT NULL UNIQUE,
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timestamp TEXT NOT NULL
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);
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""")
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self.db_conn.execute("CREATE INDEX IF NOT EXISTS idx_user_id ON memories (user_id);")
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logger.info(f"SQLite database initialized at {db_path}")
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# 4. 初始化本地 ChromaDB (存储向量)
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chroma_path = os.getenv('CHROMA_DB_PATH', './chroma_db_store')
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self.chroma_client = chromadb.PersistentClient(path=chroma_path)
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logger.info(f"ChromaDB persistent client initialized at {chroma_path}")
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# 5. 初始化本地 Embedding 模型 (低内存占用)
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model_name = 'BAAI/bge-small-zh-v1.5' # 约 100MB 内存
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try:
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logger.info(f"Loading local embedding model: {model_name}...")
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self.embedding_model = SentenceTransformer(model_name, device='cpu')
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logger.info("Local embedding model loaded successfully.")
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except Exception as e:
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logger.error(f"Failed to load embedding model '{model_name}': {e}")
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raise
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def _get_user_collection(self, user_id: str):
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"""获取或创建该用户的 ChromaDB 集合"""
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collection_name = f"user_memory_{user_id.replace('-', '_')}" # 确保 user_id
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return self.chroma_client.get_or_create_collection(name=collection_name)
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def _get_max_id(self, user_id: str):
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"""获取该用户当前最大的 memory ID"""
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try:
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cursor = self.db_conn.execute("SELECT MAX(id) FROM memories WHERE user_id = ?", (user_id,))
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max_id = cursor.fetchone()[0]
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return max_id if max_id is not None else 0
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except Exception as e:
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logger.error(f"Failed to get max id from SQLite for user {user_id}: {e}")
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return 0
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# --- 实现 Mem0Integration 的公共接口 ---
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def search_memories(self, query: str, user_id: str, limit: int = 5) -> List[Any]:
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"""
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(本地实现) 搜索相关记忆。
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返回一个字典列表，模仿 mem0 的输出格式。
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"""
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logger.debug(f"Searching local memories for user {user_id} with query: {query}")
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try:
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collection = self._get_user_collection(user_id)
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if collection.count() == 0:
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logger.debug("No memories found for this user.")
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return []
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# 1. 本地向量化查询
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query_embedding = self.embedding_model.encode(query).tolist()
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# 2. 本地 ChromaDB (KNN) 搜索
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results = collection.query(
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query_embeddings=[query_embedding],
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n_results=min(limit, collection.count()) # 确保 n_results 不超过集合大小
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)
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# 3. 格式化为原函数期望的格式
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formatted_results = []
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if results and 'metadatas' in results and 'documents' in results:
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for doc, meta in zip(results['documents'][0], results['metadatas'][0]):
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formatted_results.append({
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"memory": doc, # format_memories_for_prompt 会用这个 key
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"content": meta.get("content", doc), # 备用
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"role": meta.get("role", "unknown")
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})
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logger.debug(f"Found {len(formatted_results)} local memories.")
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return formatted_results
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except Exception as e:
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logger.error(f"Failed to search local memories: {e}")
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return []
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def add_memory(self, messages: List[Dict[str, str]], user_id: str, metadata: Optional[Dict] = None) -> Dict[str, Any]:
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"""(本地实现) 添加新的记忆。"""
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logger.debug(f"Adding local memory for user {user_id}")
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if metadata is None:
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metadata = {}
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try:
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collection = self._get_user_collection(user_id)
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texts_to_embed = []
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docs_to_store = []
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metadatas_to_store = []
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ids_to_store = []
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current_max_id = self._get_max_id(user_id)
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for i, msg in enumerate(messages):
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role = msg.get("role")
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content = msg.get("content")
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if not role or not content:
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continue
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current_max_id += 1
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chroma_id = f"mem_{user_id}_{current_max_id}"
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timestamp = metadata.get("timestamp", datetime.now().isoformat())
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# 1. 存入 SQLite
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self.db_conn.execute(
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"INSERT INTO memories (user_id, role, content, chroma_id, timestamp) VALUES (?, ?, ?, ?, ?)",
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(user_id, role, content, chroma_id, timestamp)
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)
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# 2. 准备存入 ChromaDB
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full_text = f"{role}: {content}" # 向量化的文本
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texts_to_embed.append(full_text)
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docs_to_store.append(full_text) # 存在 chroma 的 document 字段
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metadatas_to_store.append({ # 存在 chroma 的 metadata 字段
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"role": role,
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"content": content, # 存一份原始内容
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"timestamp": timestamp
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})
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ids_to_store.append(chroma_id)
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if not texts_to_embed:
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return {"success": True, "message": "No valid messages to add."}
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embeddings = self.embedding_model.encode(
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texts_to_embed,
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batch_size=8
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).tolist()
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# 3. 批量存入 ChromaDB
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collection.add(
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embeddings=embeddings,
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documents=docs_to_store,
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metadatas=metadatas_to_store,
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ids=ids_to_store
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)
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self.db_conn.commit()
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logger.debug(f"Successfully added {len(texts_to_embed)} local memories.")
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return {"success": True, "message": f"Added {len(texts_to_embed)} memories."}
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except Exception as e:
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logger.error(f"Error in add_memory (local): {e}")
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self.db_conn.rollback()
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return {"success": False, "error": str(e)}
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# --- 保留你原有的辅助函数和核心逻辑 ---
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def _extract_reply_for_memory(self, assistant_response: Any) -> str:
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"""(原样保留) 从结构化响应中提取用于记忆的助手回复文本。"""
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if assistant_response is None:
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return ""
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if not isinstance(assistant_response, str):
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assistant_response = str(assistant_response)
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raw_text = assistant_response.strip()
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if not raw_text:
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return ""
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try:
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# 尝试解析 JSON, 兼容你 `chat_service` 可能遇到的 "```json\n{...}\n```"
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if raw_text.startswith("```json"):
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raw_text = raw_text.split("```json\n", 1)[-1].split("\n```")[0]
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data = json.loads(raw_text)
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reply = data.get("reply", "")
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reply_str = str(reply).strip()
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# 如果 'reply' 为空，也返回原始文本，以防 LLM 返回了非 JSON 格式的有效回复
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return reply_str if reply_str else raw_text
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except Exception:
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# 如果解析失败，返回原始文本
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return raw_text
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def format_memories_for_prompt(self, memories: List[Any]) -> str:
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"""
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(轻微修改) 将记忆格式化为项目符号点，以便注入系统提示。
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严格遵守 Prompt 中的 "MEM_INJECT_TOPK: 1–2 条" 规则。
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"""
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if not memories:
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return ""
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formatted = []
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# 只取最相关的 2 条
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for memory in memories[:2]:
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if isinstance(memory, dict):
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# 我们的 search_memories 返回 'memory' 键
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memory_text = memory.get("memory") or memory.get("content") or ""
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elif isinstance(memory, str):
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memory_text = memory
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else:
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memory_text = str(memory)
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sanitized = " ".join(str(memory_text).split())
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if sanitized:
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formatted.append(f"- {sanitized}")
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return "\n".join(formatted)
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def generate_response_with_memory(self, user_input: str, user_id: str) -> Dict[str, Any]:
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"""
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(逻辑保留，调用本地) 使用记忆生成响应并存储交互。
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"""
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# 步骤 1: 搜索相关记忆 (调用我们的本地搜索)
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# Prompt 建议 1-2 条，我们搜 5 条，format_memories_for_prompt 会帮我们筛选
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memories = self.search_memories(user_input, user_id, limit=5)
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# 步骤 2: 准备系统提示 (逻辑保留)
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memory_block = self.format_memories_for_prompt(memories)
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system_prompt = self.system_prompt_template.replace(
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"{memory_block}", memory_block if memory_block else ""
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).strip()
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# 步骤 3: 生成响应 (逻辑保留 - 仍然调用豆包 LLM)
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try:
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response = self.openai_client.chat.completions.create(
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model=self.llm_model,
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messages=[
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{"role": "system", "content": system_prompt},
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{"role": "user", "content": user_input}
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],
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# reasoning_effort="minimal", # 你原始代码里的，如果豆包不支持就删掉
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# 推荐开启 JSON 模式，让豆包强制输出 JSON
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response_format={"type": "json_object"}
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)
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assistant_response = response.choices[0].message.content
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reply_for_memory = self._extract_reply_for_memory(assistant_response)
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if not reply_for_memory: # 确保不存空记忆
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reply_for_memory = assistant_response
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# 步骤 5: 异步存储交互 (逻辑保留 - 调用我们的本地 add_memory)
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messages = [
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{"role": "user", "content": user_input},
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{"role": "assistant", "content": reply_for_memory} # 只存回复文本
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]
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metadata = {
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"timestamp": datetime.now().isoformat(),
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"type": "chat_interaction"
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}
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|
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# (原样保留) 启动异步线程存储记忆
|
||
def store_memory_async():
|
||
try:
|
||
self.add_memory(messages, user_id, metadata)
|
||
except Exception as e:
|
||
# 原代码是 print, 统一用 logger
|
||
logger.warning(f"[WARNING] Async local memory storage failed: {e}")
|
||
|
||
memory_thread = threading.Thread(target=store_memory_async, daemon=True)
|
||
memory_thread.start()
|
||
|
||
return {
|
||
"success": True,
|
||
"response": assistant_response, # 返回完整的 JSON 字符串给 chat_service
|
||
"user_id": user_id
|
||
}
|
||
|
||
except Exception as e:
|
||
logger.error(f"[ERROR] Failed to generate response: {e}")
|
||
return {
|
||
"success": False,
|
||
"error": str(e),
|
||
"user_id": user_id
|
||
}
|
||
|
||
# --- 其他辅助 API 的本地实现 ---
|
||
|
||
def get_all_memories(self, user_id: str) -> List[Any]:
|
||
"""(本地实现) 从 SQLite 获取所有原始文本历史"""
|
||
logger.debug(f"Getting all local memories for user {user_id}")
|
||
try:
|
||
cursor = self.db_conn.execute(
|
||
"SELECT id, role, content, timestamp, chroma_id FROM memories WHERE user_id = ? ORDER BY id ASC",
|
||
(user_id,)
|
||
)
|
||
return [{
|
||
"id": row[4], # 返回 chroma_id 作为唯一标识符
|
||
"sqlite_id": row[0],
|
||
"memory": f"{row[1]}: {row[2]}",
|
||
"role": row[1],
|
||
"content": row[2],
|
||
"timestamp": row[3]
|
||
} for row in cursor.fetchall()]
|
||
except Exception as e:
|
||
logger.error(f"Error in get_all_memories (local): {e}")
|
||
return []
|
||
|
||
def delete_memory(self, memory_id: str) -> bool:
|
||
"""(本地实现) 删除特定记忆 (memory_id 应该是 chroma_id)"""
|
||
logger.debug(f"Deleting local memory: {memory_id}")
|
||
try:
|
||
# 1. 从 SQLite 删除
|
||
cursor = self.db_conn.execute("SELECT user_id FROM memories WHERE chroma_id = ?", (memory_id,))
|
||
row = cursor.fetchone()
|
||
if not row:
|
||
logger.warning(f"Memory ID {memory_id} not found in SQLite.")
|
||
return False
|
||
|
||
user_id = row[0]
|
||
self.db_conn.execute("DELETE FROM memories WHERE chroma_id = ?", (memory_id,))
|
||
|
||
# 2. 从 ChromaDB 删除
|
||
collection = self._get_user_collection(user_id)
|
||
collection.delete(ids=[memory_id])
|
||
|
||
self.db_conn.commit()
|
||
return True
|
||
except Exception as e:
|
||
logger.error(f"Failed to delete memory (local): {e}")
|
||
self.db_conn.rollback()
|
||
return False
|
||
|
||
def delete_all_memories(self, user_id: str) -> bool:
|
||
"""(本地实现) 删除用户的所有记忆"""
|
||
logger.debug(f"Deleting all local memories for user {user_id}")
|
||
try:
|
||
# 1. 从 SQLite 删除
|
||
self.db_conn.execute("DELETE FROM memories WHERE user_id = ?", (user_id,))
|
||
self.db_conn.commit()
|
||
|
||
# 2. 从 ChromaDB 删除 (删除整个 collection)
|
||
collection_name = f"user_memory_{user_id.replace('-', '_')}"
|
||
self.chroma_client.delete_collection(name=collection_name)
|
||
|
||
return True
|
||
except Exception as e:
|
||
logger.error(f"Failed to delete all memories (local): {e}")
|
||
self.db_conn.rollback()
|
||
return False
|